{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 基于鸢尾花数据进行分类模型构建，使用logistics算法和KNN算法进行构建，并计算两种算法的AOC值，以及画出对应的ORC曲线"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Iris数据集是常用的分类实验数据集，由Fisher, 1936收集整理。Iris也称鸢尾花卉数据集，是一类多重变量分析的数据集。数据集包含150个数据集，分为3类，每类50个数据，每个数据包含4个属性。可通过花萼长度，花萼宽度，花瓣长度，花瓣宽度4个属性预测鸢尾花卉属于（Setosa，Versicolour，Virginica）三个种类中的哪一类。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import warnings\n",
    "import sklearn\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "from sklearn.linear_model.coordinate_descent import ConvergenceWarning\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 设置字符集，防止中文乱码\n",
    "mpl.rcParams['font.sans-serif']=[u'simHei']\n",
    "mpl.rcParams['axes.unicode_minus']=False\n",
    "## 拦截异常\n",
    "warnings.filterwarnings(action = 'ignore', category=ConvergenceWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris-virginica     50\n",
      "Iris-versicolor    50\n",
      "Iris-setosa        50\n",
      "Name: cla, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 数据加载\n",
    "path = \"datas/iris.data\"\n",
    "names = ['sepal length', 'sepal width', 'petal length', 'petal width', 'cla']\n",
    "df = pd.read_csv(path, header=None, names=names)\n",
    "print(df['cla'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def parseRecord(record):\n",
    "    result=[]\n",
    "    r = zip(names,record)\n",
    "    for name,v in r:\n",
    "        if name == 'cla':\n",
    "            if v == 'Iris-setosa':\n",
    "                result.append(1)\n",
    "            elif v == 'Iris-versicolor':\n",
    "                result.append(2)\n",
    "            elif v == 'Iris-virginica':\n",
    "                result.append(3)\n",
    "            else:\n",
    "                result.append(np.nan)\n",
    "        else:\n",
    "            result.append(float(v))\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   sepal length  sepal width  petal length  petal width\n",
      "0           5.1          3.5           1.4          0.2\n",
      "1           4.9          3.0           1.4          0.2\n",
      "2           4.7          3.2           1.3          0.2\n",
      "3           4.6          3.1           1.5          0.2\n",
      "4           5.0          3.6           1.4          0.2\n",
      "0    1.0\n",
      "1    1.0\n",
      "2    1.0\n",
      "3    1.0\n",
      "4    1.0\n",
      "Name: cla, dtype: float64\n",
      "3.0    50\n",
      "2.0    50\n",
      "1.0    50\n",
      "Name: cla, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "## 1. 数据转换为数字以及分割\n",
    "# 数据转换\n",
    "datas = df.apply(lambda r: parseRecord(r), axis=1)\n",
    "# 异常数据删除\n",
    "datas = datas.dropna(how='any')\n",
    "# 数据分割\n",
    "X = datas[names[0:-1]]\n",
    "Y = datas[names[-1]]\n",
    "# 数据抽样(训练数据和测试数据分割)\n",
    "print(X.head())\n",
    "print(Y.head())\n",
    "print(Y.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始数据条数:150；训练数据条数:90；特征个数:4；测试样本条数:60\n",
      "(90, 4)\n",
      "(60, 4)\n"
     ]
    }
   ],
   "source": [
    "X_train,X_test,Y_train,Y_test = train_test_split(X, Y, test_size=0.4, random_state=0)\n",
    "print(\"原始数据条数:%d；训练数据条数:%d；特征个数:%d；测试样本条数:%d\" % (len(X), len(X_train), X_train.shape[1], X_test.shape[0]))\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# print(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## 2. 数据化处理\n",
    "ss = StandardScaler()\n",
    "X_train = ss.fit_transform(X_train)\n",
    "X_test = ss.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# print(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## 3. 特征选择(特征太少，这里不进行特征选择操作)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## 4. 降维处理(这里不做降维处理)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegressionCV(Cs=array([  1.00000e-04,   1.26486e-04,   1.59986e-04,   2.02359e-04,\n",
       "         2.55955e-04,   3.23746e-04,   4.09492e-04,   5.17947e-04,\n",
       "         6.55129e-04,   8.28643e-04,   1.04811e-03,   1.32571e-03,\n",
       "         1.67683e-03,   2.12095e-03,   2.68270e-03,   3.39322e-03,\n",
       "         4.29193e-03,   5.428...     3.08884e+00,   3.90694e+00,   4.94171e+00,   6.25055e+00,\n",
       "         7.90604e+00,   1.00000e+01]),\n",
       "           class_weight=None, cv=3, dual=False, fit_intercept=True,\n",
       "           intercept_scaling=1.0, max_iter=100, multi_class='multinomial',\n",
       "           n_jobs=1, penalty='l2', random_state=None, refit=True,\n",
       "           scoring=None, solver='lbfgs', tol=0.01, verbose=0)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 5. 模型构建\n",
    "lr = LogisticRegressionCV(Cs=np.logspace(-4,1,50), cv=3, fit_intercept=True, penalty='l2', solver='lbfgs', tol=0.01, multi_class='multinomial')\n",
    "lr.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic算法R值： 0.977777777778\n",
      "Logistic算法AUC值： 0.926944444444\n",
      "y_test_hot.ravel(): [0 0 1 0 1 0 1 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0\n",
      " 1 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 1 0 0 0 0 1 0 1 0 1 0 0 1 0 0 0 0\n",
      " 1 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 0 1 0 1 0 1 0 0 0 0 1 0 0 1 0 1 0 1 0 0\n",
      " 0 1 0 0 1 0 0 1 0 0 0 1 1 0 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0 1 0 0 1 0 0 1 0\n",
      " 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1]\n",
      "lr_y_score.ravel(): [ -6.27937676   1.05548892   5.22388784  -2.5371109    4.02213826\n",
      "  -1.48502736   9.58561223   2.6059998  -12.19161203  -8.18346495\n",
      "   2.6530125    5.53045244   8.06522513   3.206342   -11.27156713\n",
      "  -7.22184333   0.48250241   6.73934092   8.3061655    2.99869891\n",
      " -11.30486441  -3.63926189   2.64337134   0.99589054  -4.44558943\n",
      "   3.22131538   1.22427405  -1.96604656   2.94686917  -0.98082261\n",
      "  -4.96636124   2.69911013   2.26725111  -2.62637732   2.30702815\n",
      "   0.31934917  -2.60962466   2.90343809  -0.29381343  -4.0043684\n",
      "   2.89099073   1.11337767  -2.93851571   2.52286105   0.41565466\n",
      "   7.70822708   3.63929131 -11.34751839  -2.8118009    2.36157169\n",
      "   0.4502292   -1.8045935    2.73019664  -0.92560314   7.12845508\n",
      "   3.45997993 -10.58843501   8.46288289   2.68843117 -11.15131406\n",
      "  -4.49448454   1.43394608   3.06053846  -1.91130978   1.89262974\n",
      "   0.01868004   7.84043823   2.94114447 -10.7815827    7.78617371\n",
      "   3.37763355 -11.16380726  -4.70281021   2.21479818   2.48801203\n",
      "   9.91131131   2.72673147 -12.63804279   7.85422387   2.42150625\n",
      " -10.27573012  -2.25629084   2.82839377  -0.57210293   0.25066183\n",
      "   3.21437436  -3.46503619   7.33003213   2.93259577 -10.2626279\n",
      "  -5.14664337   1.82293543   3.32370794  -1.59680356   1.72359445\n",
      "  -0.12679089   8.02219758   3.24450592 -11.2667035   -4.13742303\n",
      "   1.84449723   2.29292581  -7.2966421    1.69209766   5.60454444\n",
      "  -0.98760882   2.21626947  -1.22866065   7.48053662   3.10154607\n",
      " -10.5820827   -4.46388278   2.35421825   2.10966453  -1.97859927\n",
      "   2.21830435  -0.23970508  -1.73705971   3.07991182  -1.3428521\n",
      "  -6.94768906   1.91731888   5.03037018   8.26367368   3.26261293\n",
      " -11.52628661  -6.8945401    1.71742173   5.17711837   7.00114109\n",
      "   2.55201873  -9.55315982   8.2506487    3.15179846 -11.40244716\n",
      "  -0.83342074   3.55890736  -2.72548661  -5.57537515   2.03832445\n",
      "   3.53705071  -7.44638593   0.89269467   6.55369126  -5.386268     3.1574685\n",
      "   2.2287995   -7.65174457   2.78959492   4.86214965  -1.25902403\n",
      "   3.19064129  -1.93161727  -9.77227645   2.28402866   7.48824779\n",
      "  -2.76747696   1.91870518   0.84877178  -1.23842631   3.26103558\n",
      "  -2.02260927  -5.26636527   2.40614663   2.86021863  -4.36397858\n",
      "   2.60175264   1.76222594  -2.76830109   1.70565742   1.06264367\n",
      "  -6.24433075   2.51899599   3.72533476  -3.53935769   2.89090574\n",
      "   0.64845195  -5.77127503   1.86074046   3.91053457]\n",
      "lr_fpr: [ 0.          0.          0.00833333  0.00833333  0.025       0.025\n",
      "  0.03333333  0.03333333  0.04166667  0.04166667  0.05        0.05        0.075\n",
      "  0.075       0.08333333  0.08333333  0.1         0.1         0.10833333\n",
      "  0.10833333  0.14166667  0.14166667  0.16666667  0.16666667  0.175       0.175\n",
      "  0.19166667  0.19166667  0.2         0.2         0.225       0.225\n",
      "  0.23333333  0.23333333  0.25833333  0.25833333  0.30833333  0.30833333\n",
      "  1.        ]\n",
      "lr_tpr: [ 0.01666667  0.46666667  0.46666667  0.5         0.5         0.51666667\n",
      "  0.51666667  0.53333333  0.53333333  0.56666667  0.56666667  0.58333333\n",
      "  0.58333333  0.61666667  0.61666667  0.63333333  0.63333333  0.71666667\n",
      "  0.71666667  0.73333333  0.73333333  0.75        0.75        0.76666667\n",
      "  0.76666667  0.78333333  0.78333333  0.85        0.85        0.91666667\n",
      "  0.91666667  0.93333333  0.93333333  0.95        0.95        0.98333333\n",
      "  0.98333333  1.          1.        ]\n",
      "lr_threasholds:  [  9.91131131   3.72533476   3.63929131   3.53705071   3.37763355\n",
      "   3.32370794   3.26261293   3.26103558   3.24450592   3.21437436\n",
      "   3.206342     3.19064129   3.10154607   3.06053846   2.99869891\n",
      "   2.94686917   2.93259577   2.82839377   2.78959492   2.73019664\n",
      "   2.6530125    2.64337134   2.55201873   2.52286105   2.51899599\n",
      "   2.48801203   2.40614663   2.29292581   2.28402866   2.21626947\n",
      "   2.03832445   1.91870518   1.91731888   1.89262974   1.82293543\n",
      "   1.72359445   1.11337767   1.06264367 -12.63804279]\n"
     ]
    }
   ],
   "source": [
    "## 6. 模型效果输出\n",
    "# 用hot编码将正确的数据转换为矩阵形式\n",
    "y_test_hot = label_binarize(Y_test,classes=(1,2,3))\n",
    "# 得到预测的损失值\n",
    "# 得分？\n",
    "lr_y_score = lr.decision_function(X_test)\n",
    "# 计算roc的值\n",
    "# http://blog.csdn.net/hh1294212648/article/details/77649127\n",
    "lr_fpr, lr_tpr, lr_threasholds = metrics.roc_curve(y_test_hot.ravel(),lr_y_score.ravel())\n",
    "# 计算auc的值\n",
    "lr_auc = metrics.auc(lr_fpr, lr_tpr)\n",
    "print(\"Logistic算法R值：\", lr.score(X_train, Y_train))\n",
    "print(\"Logistic算法AUC值：\", lr_auc)\n",
    "print(\"y_test_hot.ravel():\", y_test_hot.ravel())\n",
    "print(\"lr_y_score.ravel():\", lr_y_score.ravel())\n",
    "print(\"lr_fpr:\", lr_fpr)\n",
    "print(\"lr_tpr:\", lr_tpr)\n",
    "print(\"lr_threasholds: \",lr_threasholds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## 7. 模型预测\n",
    "lr_y_predict = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN算法R值： 0.977777777778\n",
      "KNN算法AUC值： 0.969444444444\n",
      "[[ 0.          0.          1.        ]\n",
      " [ 0.          1.          0.        ]\n",
      " [ 1.          0.          0.        ]\n",
      " [ 0.          0.          1.        ]\n",
      " [ 1.          0.          0.        ]\n",
      " [ 0.          0.          1.        ]\n",
      " [ 1.          0.          0.        ]\n",
      " [ 0.          1.          0.        ]\n",
      " [ 0.          1.          0.        ]\n",
      " [ 0.          1.          0.        ]\n",
      " [ 0.          0.33333333  0.66666667]\n",
      " [ 0.          1.          0.        ]\n",
      " [ 0.          1.          0.        ]\n",
      " [ 0.          1.          0.        ]\n",
      " [ 0.          1.          0.        ]\n",
      " [ 1.          0.          0.        ]\n",
      " [ 0.          0.66666667  0.33333333]\n",
      " [ 0.          1.          0.        ]\n",
      " [ 1.          0.          0.        ]\n",
      " [ 1.          0.          0.        ]\n",
      " [ 0.          0.          1.        ]\n",
      " [ 0.          1.          0.        ]\n",
      " [ 1.          0.          0.        ]\n",
      " [ 1.          0.          0.        ]\n",
      " [ 0.          0.          1.        ]\n",
      " [ 1.          0.          0.        ]\n",
      " [ 1.          0.          0.        ]\n",
      " [ 0.          1.          0.        ]\n",
      " [ 0.          1.          0.        ]\n",
      " [ 1.          0.          0.        ]\n",
      " [ 0.          0.33333333  0.66666667]\n",
      " [ 0.          1.          0.        ]\n",
      " [ 1.          0.          0.        ]\n",
      " [ 0.          0.          1.        ]\n",
      " [ 0.          0.          1.        ]\n",
      " [ 0.          1.          0.        ]\n",
      " [ 1.          0.          0.        ]\n",
      " [ 0.          0.          1.        ]\n",
      " [ 0.          1.          0.        ]\n",
      " [ 0.          1.          0.        ]\n",
      " [ 0.          0.          1.        ]\n",
      " [ 1.          0.          0.        ]\n",
      " [ 0.          0.          1.        ]\n",
      " [ 1.          0.          0.        ]\n",
      " [ 1.          0.          0.        ]\n",
      " [ 0.          1.          0.        ]\n",
      " [ 0.          0.33333333  0.66666667]\n",
      " [ 0.          0.          1.        ]\n",
      " [ 0.          1.          0.        ]\n",
      " [ 0.          0.33333333  0.66666667]\n",
      " [ 0.          1.          0.        ]\n",
      " [ 0.          0.          1.        ]\n",
      " [ 0.          0.66666667  0.33333333]\n",
      " [ 0.          1.          0.        ]\n",
      " [ 0.          0.          1.        ]\n",
      " [ 0.          0.66666667  0.33333333]\n",
      " [ 0.          0.66666667  0.33333333]\n",
      " [ 0.          0.33333333  0.66666667]\n",
      " [ 0.          1.          0.        ]\n",
      " [ 0.          0.33333333  0.66666667]]\n",
      "[ 0.          0.01666667  0.03333333  0.1         1.        ]\n",
      "[ 0.          0.8         0.93333333  0.96666667  1.        ]\n"
     ]
    }
   ],
   "source": [
    "##### KNN算法实现\n",
    "# a. 模型构建\n",
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(X_train, Y_train)\n",
    "\n",
    "# b. 模型效果输出\n",
    "## 将正确的数据转换为矩阵形式\n",
    "y_test_hot = label_binarize(Y_test,classes=(1,2,3))\n",
    "## 得到预测的损失值\n",
    "knn_y_score = knn.predict_proba(X_test)\n",
    "## 计算roc的值，knn_threasholds阈值\n",
    "knn_fpr, knn_tpr, knn_threasholds = metrics.roc_curve(y_test_hot.ravel(),knn_y_score.ravel())\n",
    "## 计算auc的值\n",
    "knn_auc = metrics.auc(knn_fpr, knn_tpr)\n",
    "print(\"KNN算法R值：\", knn.score(X_train, Y_train))\n",
    "print(\"KNN算法AUC值：\", knn_auc)\n",
    "\n",
    "# c. 模型预测\n",
    "knn_y_predict = knn.predict(X_test)\n",
    "# 概率？\n",
    "print(knn_y_score)\n",
    "print(knn_fpr)\n",
    "print(knn_tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Mnz8fv/zyS6HHJq1s79u3L0aPHg1vb2+kpaVh9uzZ+bYtX748goODQURYuXKl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\nEEIIIUTIkmJWCCGEEEKErP8AnGJRO0HIGkcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb26e4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 画图1：ROC曲线画图\n",
    "plt.figure(figsize=(8, 6), facecolor='w')\n",
    "plt.plot(lr_fpr,lr_tpr,c='r',lw=2,label=u'Logistic算法,AUC=%.3f' % lr_auc)\n",
    "plt.plot(knn_fpr,knn_tpr,c='g',lw=2,label=u'KNN算法,AUC=%.3f' % knn_auc)\n",
    "plt.plot((0,1),(0,1),c='#a0a0a0',lw=2,ls='--')\n",
    "plt.xlim(-0.01, 1.02) # 设置x轴的最小值和最大值\n",
    "plt.ylim(-0.01, 1.02)\n",
    "plt.xticks(np.arange(0, 1.1, 0.1))\n",
    "plt.yticks(np.arange(0, 1.1, 0.1))\n",
    "plt.xlabel('False Positive Rate(FPR)', fontsize=16)\n",
    "plt.ylabel('True Positive Rate(TPR)', fontsize=16)\n",
    "plt.grid(b=True, ls=':')\n",
    "plt.legend(loc='lower right', fancybox=True, framealpha=0.8, fontsize=12)\n",
    "plt.title(u'鸢尾花数据Logistic和KNN算法的ROC/AUC', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AJYRtkMTERHvdunV2QkJCcZcCACiBkpOT7a+++speuHCh/fzzz9t+fn62r6+v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\nvK1fv141ahzO8o/y2CnnHUA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a14Kp3mlpabrxlhu1I3aH52fReWWH7w1XxJc2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\nsrJ0ySWX+FIcAAAAAAA4j9eJ/erVq9WxY0dZrVb17dtXffv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t5Y96m9rXTP0sCdV6+8rk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\nYk9ERERERERUjDGxJyIiIiIiIirG/h9j+o7ctdVx2QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb637a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 画图2：预测结果画图\n",
    "x_test_len = range(len(X_test))\n",
    "plt.figure(figsize=(12, 9), facecolor='w')\n",
    "plt.ylim(0.5,3.5)\n",
    "plt.plot(x_test_len, Y_test, 'ro',markersize = 6, zorder=3, label=u'真实值')\n",
    "plt.plot(x_test_len, lr_y_predict, 'go', markersize = 10, zorder=2, label=u'Logis算法预测值,$R^2$=%.3f' % lr.score(X_test, Y_test))\n",
    "plt.plot(x_test_len, knn_y_predict, 'yo', markersize = 16, zorder=1, label=u'KNN算法预测值,$R^2$=%.3f' % knn.score(X_test, Y_test))\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.xlabel(u'数据编号', fontsize=18)\n",
    "plt.ylabel(u'种类', fontsize=18)\n",
    "plt.title(u'鸢尾花数据分类', fontsize=20)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
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